

'''simple docstring'''

import argparse
import json
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    ConditionalDetrConfig,
    ConditionalDetrForObjectDetection,
    ConditionalDetrForSegmentation,
    ConditionalDetrImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE							:				Optional[int]     	=			logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
SCREAMING_SNAKE_CASE							:				List[Any]     	=			[]
for i in range(6):
							# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
							rename_keys.append(
							    (f"transformer.encoder.layers.{i}.self_attn.out_proj.weight", f"encoder.layers.{i}.self_attn.out_proj.weight")
							)
							rename_keys.append(
							    (f"transformer.encoder.layers.{i}.self_attn.out_proj.bias", f"encoder.layers.{i}.self_attn.out_proj.bias")
							)
							rename_keys.append((f"transformer.encoder.layers.{i}.linear1.weight", f"encoder.layers.{i}.fc1.weight"))
							rename_keys.append((f"transformer.encoder.layers.{i}.linear1.bias", f"encoder.layers.{i}.fc1.bias"))
							rename_keys.append((f"transformer.encoder.layers.{i}.linear2.weight", f"encoder.layers.{i}.fc2.weight"))
							rename_keys.append((f"transformer.encoder.layers.{i}.linear2.bias", f"encoder.layers.{i}.fc2.bias"))
							rename_keys.append(
							    (f"transformer.encoder.layers.{i}.norm1.weight", f"encoder.layers.{i}.self_attn_layer_norm.weight")
							)
							rename_keys.append((f"transformer.encoder.layers.{i}.norm1.bias", f"encoder.layers.{i}.self_attn_layer_norm.bias"))
							rename_keys.append((f"transformer.encoder.layers.{i}.norm2.weight", f"encoder.layers.{i}.final_layer_norm.weight"))
							rename_keys.append((f"transformer.encoder.layers.{i}.norm2.bias", f"encoder.layers.{i}.final_layer_norm.bias"))
							# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
							rename_keys.append(
							    (f"transformer.decoder.layers.{i}.self_attn.out_proj.weight", f"decoder.layers.{i}.self_attn.out_proj.weight")
							)
							rename_keys.append(
							    (f"transformer.decoder.layers.{i}.self_attn.out_proj.bias", f"decoder.layers.{i}.self_attn.out_proj.bias")
							)
							rename_keys.append(
							    (
							        f"transformer.decoder.layers.{i}.cross_attn.out_proj.weight",
							        f"decoder.layers.{i}.encoder_attn.out_proj.weight",
							    )
							)
							rename_keys.append(
							    (
							        f"transformer.decoder.layers.{i}.cross_attn.out_proj.bias",
							        f"decoder.layers.{i}.encoder_attn.out_proj.bias",
							    )
							)
							rename_keys.append((f"transformer.decoder.layers.{i}.linear1.weight", f"decoder.layers.{i}.fc1.weight"))
							rename_keys.append((f"transformer.decoder.layers.{i}.linear1.bias", f"decoder.layers.{i}.fc1.bias"))
							rename_keys.append((f"transformer.decoder.layers.{i}.linear2.weight", f"decoder.layers.{i}.fc2.weight"))
							rename_keys.append((f"transformer.decoder.layers.{i}.linear2.bias", f"decoder.layers.{i}.fc2.bias"))
							rename_keys.append(
							    (f"transformer.decoder.layers.{i}.norm1.weight", f"decoder.layers.{i}.self_attn_layer_norm.weight")
							)
							rename_keys.append((f"transformer.decoder.layers.{i}.norm1.bias", f"decoder.layers.{i}.self_attn_layer_norm.bias"))
							rename_keys.append(
							    (f"transformer.decoder.layers.{i}.norm2.weight", f"decoder.layers.{i}.encoder_attn_layer_norm.weight")
							)
							rename_keys.append(
							    (f"transformer.decoder.layers.{i}.norm2.bias", f"decoder.layers.{i}.encoder_attn_layer_norm.bias")
							)
							rename_keys.append((f"transformer.decoder.layers.{i}.norm3.weight", f"decoder.layers.{i}.final_layer_norm.weight"))
							rename_keys.append((f"transformer.decoder.layers.{i}.norm3.bias", f"decoder.layers.{i}.final_layer_norm.bias"))

							# q, k, v projections in self/cross-attention in decoder for conditional DETR
							rename_keys.append(
							    (f"transformer.decoder.layers.{i}.sa_qcontent_proj.weight", f"decoder.layers.{i}.sa_qcontent_proj.weight")
							)
							rename_keys.append(
							    (f"transformer.decoder.layers.{i}.sa_kcontent_proj.weight", f"decoder.layers.{i}.sa_kcontent_proj.weight")
							)
							rename_keys.append(
							    (f"transformer.decoder.layers.{i}.sa_qpos_proj.weight", f"decoder.layers.{i}.sa_qpos_proj.weight")
							)
							rename_keys.append(
							    (f"transformer.decoder.layers.{i}.sa_kpos_proj.weight", f"decoder.layers.{i}.sa_kpos_proj.weight")
							)
							rename_keys.append((f"transformer.decoder.layers.{i}.sa_v_proj.weight", f"decoder.layers.{i}.sa_v_proj.weight"))
							rename_keys.append(
							    (f"transformer.decoder.layers.{i}.ca_qcontent_proj.weight", f"decoder.layers.{i}.ca_qcontent_proj.weight")
							)
							# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.weight", f"decoder.layers.{i}.ca_qpos_proj.weight"))
							rename_keys.append(
							    (f"transformer.decoder.layers.{i}.ca_kcontent_proj.weight", f"decoder.layers.{i}.ca_kcontent_proj.weight")
							)
							rename_keys.append(
							    (f"transformer.decoder.layers.{i}.ca_kpos_proj.weight", f"decoder.layers.{i}.ca_kpos_proj.weight")
							)
							rename_keys.append((f"transformer.decoder.layers.{i}.ca_v_proj.weight", f"decoder.layers.{i}.ca_v_proj.weight"))
							rename_keys.append(
							    (f"transformer.decoder.layers.{i}.ca_qpos_sine_proj.weight", f"decoder.layers.{i}.ca_qpos_sine_proj.weight")
							)

							rename_keys.append(
							    (f"transformer.decoder.layers.{i}.sa_qcontent_proj.bias", f"decoder.layers.{i}.sa_qcontent_proj.bias")
							)
							rename_keys.append(
							    (f"transformer.decoder.layers.{i}.sa_kcontent_proj.bias", f"decoder.layers.{i}.sa_kcontent_proj.bias")
							)
							rename_keys.append((f"transformer.decoder.layers.{i}.sa_qpos_proj.bias", f"decoder.layers.{i}.sa_qpos_proj.bias"))
							rename_keys.append((f"transformer.decoder.layers.{i}.sa_kpos_proj.bias", f"decoder.layers.{i}.sa_kpos_proj.bias"))
							rename_keys.append((f"transformer.decoder.layers.{i}.sa_v_proj.bias", f"decoder.layers.{i}.sa_v_proj.bias"))
							rename_keys.append(
							    (f"transformer.decoder.layers.{i}.ca_qcontent_proj.bias", f"decoder.layers.{i}.ca_qcontent_proj.bias")
							)
							# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.bias", f"decoder.layers.{i}.ca_qpos_proj.bias"))
							rename_keys.append(
							    (f"transformer.decoder.layers.{i}.ca_kcontent_proj.bias", f"decoder.layers.{i}.ca_kcontent_proj.bias")
							)
							rename_keys.append((f"transformer.decoder.layers.{i}.ca_kpos_proj.bias", f"decoder.layers.{i}.ca_kpos_proj.bias"))
							rename_keys.append((f"transformer.decoder.layers.{i}.ca_v_proj.bias", f"decoder.layers.{i}.ca_v_proj.bias"))
							rename_keys.append(
							    (f"transformer.decoder.layers.{i}.ca_qpos_sine_proj.bias", f"decoder.layers.{i}.ca_qpos_sine_proj.bias")
							)

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
# for conditional DETR, also convert reference point head and query scale MLP
rename_keys.extend(
    [
        ("input_proj.weight", "input_projection.weight"),
        ("input_proj.bias", "input_projection.bias"),
        ("query_embed.weight", "query_position_embeddings.weight"),
        ("transformer.decoder.norm.weight", "decoder.layernorm.weight"),
        ("transformer.decoder.norm.bias", "decoder.layernorm.bias"),
        ("class_embed.weight", "class_labels_classifier.weight"),
        ("class_embed.bias", "class_labels_classifier.bias"),
        ("bbox_embed.layers.0.weight", "bbox_predictor.layers.0.weight"),
        ("bbox_embed.layers.0.bias", "bbox_predictor.layers.0.bias"),
        ("bbox_embed.layers.1.weight", "bbox_predictor.layers.1.weight"),
        ("bbox_embed.layers.1.bias", "bbox_predictor.layers.1.bias"),
        ("bbox_embed.layers.2.weight", "bbox_predictor.layers.2.weight"),
        ("bbox_embed.layers.2.bias", "bbox_predictor.layers.2.bias"),
        ("transformer.decoder.ref_point_head.layers.0.weight", "decoder.ref_point_head.layers.0.weight"),
        ("transformer.decoder.ref_point_head.layers.0.bias", "decoder.ref_point_head.layers.0.bias"),
        ("transformer.decoder.ref_point_head.layers.1.weight", "decoder.ref_point_head.layers.1.weight"),
        ("transformer.decoder.ref_point_head.layers.1.bias", "decoder.ref_point_head.layers.1.bias"),
        ("transformer.decoder.query_scale.layers.0.weight", "decoder.query_scale.layers.0.weight"),
        ("transformer.decoder.query_scale.layers.0.bias", "decoder.query_scale.layers.0.bias"),
        ("transformer.decoder.query_scale.layers.1.weight", "decoder.query_scale.layers.1.weight"),
        ("transformer.decoder.query_scale.layers.1.bias", "decoder.query_scale.layers.1.bias"),
        ("transformer.decoder.layers.0.ca_qpos_proj.weight", "decoder.layers.0.ca_qpos_proj.weight"),
        ("transformer.decoder.layers.0.ca_qpos_proj.bias", "decoder.layers.0.ca_qpos_proj.bias"),
    ]
)




def 							_UpperCamelCase				(   lowerCAmelCase__:					Tuple		,lowerCAmelCase__:					Dict		,lowerCAmelCase__:					List[str]   )      -> Optional[Any]:
							SCREAMING_SNAKE_CASE_				=				state_dict.pop(lowerCAmelCase__   )
							SCREAMING_SNAKE_CASE_				=				val




def 							_UpperCamelCase				(   lowerCAmelCase__:					Optional[Any]   )      -> Any:
							SCREAMING_SNAKE_CASE_				=				OrderedDict()
							for key, value in state_dict.items():
														if "backbone.0.body" in key:
																					SCREAMING_SNAKE_CASE_				=				key.replace('backbone.0.body'		,'backbone.conv_encoder.model'   )
																					SCREAMING_SNAKE_CASE_				=				value
														else:
																					SCREAMING_SNAKE_CASE_				=				value

							return new_state_dict




def 							_UpperCamelCase				(   lowerCAmelCase__:					Optional[int]		,lowerCAmelCase__:					Tuple=False   )      -> Union[str, Any]:
							SCREAMING_SNAKE_CASE_				=				''
							if is_panoptic:
														SCREAMING_SNAKE_CASE_				=				'conditional_detr.'

							# first: transformer encoder
							for i in range(6   ):
														# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
														SCREAMING_SNAKE_CASE_				=				state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""   )
														SCREAMING_SNAKE_CASE_				=				state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias"""   )
														# next, add query, keys and values (in that order) to the state dict
														SCREAMING_SNAKE_CASE_				=				in_proj_weight[:256, :]
														SCREAMING_SNAKE_CASE_				=				in_proj_bias[:256]
														SCREAMING_SNAKE_CASE_				=				in_proj_weight[256:512, :]
														SCREAMING_SNAKE_CASE_				=				in_proj_bias[256:512]
														SCREAMING_SNAKE_CASE_				=				in_proj_weight[-256:, :]
														SCREAMING_SNAKE_CASE_				=				in_proj_bias[-256:]




def 							_UpperCamelCase				(   )      -> Any:
							SCREAMING_SNAKE_CASE_				=				'http://images.cocodataset.org/val2017/000000039769.jpg'
							SCREAMING_SNAKE_CASE_				=				Image.open(requests.get(lowerCAmelCase__		,stream=lowerCAmelCase__   ).raw   )

							return im




@torch.no_grad()
def 							_UpperCamelCase				(   lowerCAmelCase__:					str		,lowerCAmelCase__:					Dict   )      -> Union[str, Any]:
							SCREAMING_SNAKE_CASE_				=				ConditionalDetrConfig()
							# set backbone and dilation attributes
							if "resnet101" in model_name:
														SCREAMING_SNAKE_CASE_				=				'resnet101'
							if "dc5" in model_name:
														SCREAMING_SNAKE_CASE_				=				True
							SCREAMING_SNAKE_CASE_				=				'panoptic' in model_name
							if is_panoptic:
														SCREAMING_SNAKE_CASE_				=				250
							else:
														SCREAMING_SNAKE_CASE_				=				91
														SCREAMING_SNAKE_CASE_				=				'huggingface/label-files'
														SCREAMING_SNAKE_CASE_				=				'coco-detection-id2label.json'
														SCREAMING_SNAKE_CASE_				=				json.load(open(hf_hub_download(lowerCAmelCase__		,lowerCAmelCase__		,repo_type='dataset'   )		,'r'   )   )
														SCREAMING_SNAKE_CASE_				=				{int(lowerCAmelCase__   ): v for k, v in idalabel.items()}
														SCREAMING_SNAKE_CASE_				=				idalabel
														SCREAMING_SNAKE_CASE_				=				{v: k for k, v in idalabel.items()}

							# load image processor
							SCREAMING_SNAKE_CASE_				=				'coco_panoptic' if is_panoptic else 'coco_detection'
							SCREAMING_SNAKE_CASE_				=				ConditionalDetrImageProcessor(format=lowerCAmelCase__   )

							# prepare image
							SCREAMING_SNAKE_CASE_				=				prepare_img()
							SCREAMING_SNAKE_CASE_				=				image_processor(images=lowerCAmelCase__		,return_tensors='pt'   )
							SCREAMING_SNAKE_CASE_				=				encoding['pixel_values']

							logger.info(F"""Converting model {model_name}..."""   )

							# load original model from torch hub
							SCREAMING_SNAKE_CASE_				=				torch.hub.load('DeppMeng/ConditionalDETR'		,lowerCAmelCase__		,pretrained=lowerCAmelCase__   ).eval()
							SCREAMING_SNAKE_CASE_				=				conditional_detr.state_dict()
							# rename keys
							for src, dest in rename_keys:
														if is_panoptic:
																					SCREAMING_SNAKE_CASE_				=				'conditional_detr.' + src
														rename_key(lowerCAmelCase__		,lowerCAmelCase__		,lowerCAmelCase__   )
							SCREAMING_SNAKE_CASE_				=				rename_backbone_keys(lowerCAmelCase__   )
							# query, key and value matrices need special treatment
							read_in_q_k_v(lowerCAmelCase__		,is_panoptic=lowerCAmelCase__   )
							# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
							SCREAMING_SNAKE_CASE_				=				'conditional_detr.model.' if is_panoptic else 'model.'
							for key in state_dict.copy().keys():
														if is_panoptic:
																					if (
																					    key.startswith('conditional_detr'   )
																					    and not key.startswith('class_labels_classifier'   )
																					    and not key.startswith('bbox_predictor'   )
																					):
																												SCREAMING_SNAKE_CASE_				=				state_dict.pop(lowerCAmelCase__   )
																												SCREAMING_SNAKE_CASE_				=				val
																					elif "class_labels_classifier" in key or "bbox_predictor" in key:
																												SCREAMING_SNAKE_CASE_				=				state_dict.pop(lowerCAmelCase__   )
																												SCREAMING_SNAKE_CASE_				=				val
																					elif key.startswith('bbox_attention'   ) or key.startswith('mask_head'   ):
																												continue
																					else:
																												SCREAMING_SNAKE_CASE_				=				state_dict.pop(lowerCAmelCase__   )
																												SCREAMING_SNAKE_CASE_				=				val
														else:
																					if not key.startswith('class_labels_classifier'   ) and not key.startswith('bbox_predictor'   ):
																												SCREAMING_SNAKE_CASE_				=				state_dict.pop(lowerCAmelCase__   )
																												SCREAMING_SNAKE_CASE_				=				val
    # finally, create HuggingFace model and load state dict
							SCREAMING_SNAKE_CASE_				=				ConditionalDetrForSegmentation(lowerCAmelCase__   ) if is_panoptic else ConditionalDetrForObjectDetection(lowerCAmelCase__   )
							model.load_state_dict(lowerCAmelCase__   )
							model.eval()
							model.push_to_hub(repo_id=lowerCAmelCase__		,organization='DepuMeng'		,commit_message='Add model'   )
							# verify our conversion
							SCREAMING_SNAKE_CASE_				=				conditional_detr(lowerCAmelCase__   )
							SCREAMING_SNAKE_CASE_				=				model(lowerCAmelCase__   )
							assert torch.allclose(outputs.logits		,original_outputs['pred_logits']		,atol=1E-4   )
							assert torch.allclose(outputs.pred_boxes		,original_outputs['pred_boxes']		,atol=1E-4   )
							if is_panoptic:
														assert torch.allclose(outputs.pred_masks		,original_outputs['pred_masks']		,atol=1E-4   )

							# Save model and image processor
							logger.info(F"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."""   )
							Path(lowerCAmelCase__   ).mkdir(exist_ok=lowerCAmelCase__   )
							model.save_pretrained(lowerCAmelCase__   )
							image_processor.save_pretrained(lowerCAmelCase__   )


if __name__ == "__main__":
							SCREAMING_SNAKE_CASE							:				int     	=			argparse.ArgumentParser()

							parser.add_argument(
							    "--model_name",
							    default="conditional_detr_resnet50",
							    type=str,
							    help="Name of the CONDITIONAL_DETR model you'd like to convert.",
							)
							parser.add_argument(
							    "--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
							)
							SCREAMING_SNAKE_CASE							:				List[str]     	=			parser.parse_args()
							convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)






"""simple docstring"""



import unittest

from diffusers.pipelines.pipeline_utils import is_safetensors_compatible






class  snake_case__      (       unittest.TestCase       ):


 def 						a__   ( self						):
        __a       =    [
            "safety_checker/pytorch_model.bin",
            "safety_checker/model.safetensors",
            "vae/diffusion_pytorch_model.bin",
            "vae/diffusion_pytorch_model.safetensors",
            "text_encoder/pytorch_model.bin",
            "text_encoder/model.safetensors",
            "unet/diffusion_pytorch_model.bin",
            "unet/diffusion_pytorch_model.safetensors",
        ]
        self.assertTrue(is_safetensors_compatible(lowerCamelCase						)						)


 def 						a__   ( self						):
        __a       =    [
            "unet/diffusion_pytorch_model.bin",
            "unet/diffusion_pytorch_model.safetensors",
        ]
        self.assertTrue(is_safetensors_compatible(lowerCamelCase						)						)


 def 						a__   ( self						):
        __a       =    [
            "safety_checker/pytorch_model.bin",
            "safety_checker/model.safetensors",
            "vae/diffusion_pytorch_model.bin",
            "vae/diffusion_pytorch_model.safetensors",
            "text_encoder/pytorch_model.bin",
            "text_encoder/model.safetensors",
            "unet/diffusion_pytorch_model.bin",
            # Removed: 'unet/diffusion_pytorch_model.safetensors',
        ]
        self.assertFalse(is_safetensors_compatible(lowerCamelCase						)						)


 def 						a__   ( self						):
        __a       =    [
            "text_encoder/pytorch_model.bin",
            "text_encoder/model.safetensors",
        ]
        self.assertTrue(is_safetensors_compatible(lowerCamelCase						)						)


 def 						a__   ( self						):
        __a       =    [
            "safety_checker/pytorch_model.bin",
            "safety_checker/model.safetensors",
            "vae/diffusion_pytorch_model.bin",
            "vae/diffusion_pytorch_model.safetensors",
            "text_encoder/pytorch_model.bin",
            # Removed: 'text_encoder/model.safetensors',
            "unet/diffusion_pytorch_model.bin",
            "unet/diffusion_pytorch_model.safetensors",
        ]
        self.assertFalse(is_safetensors_compatible(lowerCamelCase						)						)


 def 						a__   ( self						):
        __a       =    [
            "safety_checker/pytorch_model.fp16.bin",
            "safety_checker/model.fp16.safetensors",
            "vae/diffusion_pytorch_model.fp16.bin",
            "vae/diffusion_pytorch_model.fp16.safetensors",
            "text_encoder/pytorch_model.fp16.bin",
            "text_encoder/model.fp16.safetensors",
            "unet/diffusion_pytorch_model.fp16.bin",
            "unet/diffusion_pytorch_model.fp16.safetensors",
        ]
        __a       =    "fp16"
        self.assertTrue(is_safetensors_compatible(lowerCamelCase    ,		variant=lowerCamelCase						)						)


 def 						a__   ( self						):
        __a       =    [
            "unet/diffusion_pytorch_model.fp16.bin",
            "unet/diffusion_pytorch_model.fp16.safetensors",
        ]
        __a       =    "fp16"
        self.assertTrue(is_safetensors_compatible(lowerCamelCase    ,		variant=lowerCamelCase						)						)


 def 						a__   ( self						):
        # pass variant but use the non-variant filenames
        __a       =    [
            "unet/diffusion_pytorch_model.bin",
            "unet/diffusion_pytorch_model.safetensors",
        ]
        __a       =    "fp16"
        self.assertTrue(is_safetensors_compatible(lowerCamelCase    ,		variant=lowerCamelCase						)						)


 def 						a__   ( self						):
        __a       =    [
            "safety_checker/pytorch_model.fp16.bin",
            "safety_checker/model.fp16.safetensors",
            "vae/diffusion_pytorch_model.fp16.bin",
            "vae/diffusion_pytorch_model.fp16.safetensors",
            "text_encoder/pytorch_model.fp16.bin",
            "text_encoder/model.fp16.safetensors",
            "unet/diffusion_pytorch_model.fp16.bin",
            # Removed: 'unet/diffusion_pytorch_model.fp16.safetensors',
        ]
        __a       =    "fp16"
        self.assertFalse(is_safetensors_compatible(lowerCamelCase    ,		variant=lowerCamelCase						)						)


 def 						a__   ( self						):
        __a       =    [
            "text_encoder/pytorch_model.fp16.bin",
            "text_encoder/model.fp16.safetensors",
        ]
        __a       =    "fp16"
        self.assertTrue(is_safetensors_compatible(lowerCamelCase    ,		variant=lowerCamelCase						)						)


 def 						a__   ( self						):
        # pass variant but use the non-variant filenames
        __a       =    [
            "text_encoder/pytorch_model.bin",
            "text_encoder/model.safetensors",
        ]
        __a       =    "fp16"
        self.assertTrue(is_safetensors_compatible(lowerCamelCase    ,		variant=lowerCamelCase						)						)






 def 						a__   ( self						):
        __a       =    [
            "safety_checker/pytorch_model.fp16.bin",
            "safety_checker/model.fp16.safetensors",
            "vae/diffusion_pytorch_model.fp16.bin",
            "vae/diffusion_pytorch_model.fp16.safetensors",
            "text_encoder/pytorch_model.fp16.bin",
            # 'text_encoder/model.fp16.safetensors',
            "unet/diffusion_pytorch_model.fp16.bin",
            "unet/diffusion_pytorch_model.fp16.safetensors",
        ]
        __a       =    "fp16"
        self.assertFalse(is_safetensors_compatible(lowerCamelCase    ,		variant=lowerCamelCase						)						)
